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Artificial Neural Networks, due to the massively parallel structures and self-organization abilities, have been recently applied in a wide range of areas, including image processing in general and image compression in particular. Competitive neural networks have been utilised in performing Vector Quantization (VQ). Over the past decade, VQ has deve...
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In this work we present a new approach to learn compressible representations in deep architectures with an end-to-end training strategy. Our method is based on a soft relaxation of quantization and entropy, which we anneal to their discrete counterparts throughout training. We showcase this method for two challenging applications: Image compression...
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Citations
A 1-dimensional wavelet transform is a method of expansion of a single-variable function into a combination of generic functions called "wavelets". Wavelets are generated from a single appropriately selected function by operations of dilation and translation. Expansion into wavelets captures the essential time-frequency properties of a function. Recently, the 2-dimensional wavelet transform has found an application in image coding. The 2-D wavelet transform followed by vector quantization gives a possibility to encode the image data with a low bit rate without significant loss in quality. This explains the growing popularity of the wavelet transform. In this report we will cover the theoretical foundation of the wavelet transform and present its application in image coding. The problem of optimal bit allocation for quantization of wavelet coefficients will be also examined. The report will be concluded with some experimental results of this image coding. Key Words: Image Coding, Wavel...
